Current Medicinal Chemistry - Volume 26, Issue 21, 2019

Background: Discovery and development of a new drug is a long lasting and expensive journey that takes around 20 years from starting idea to approval and marketing of new medication. Despite R expenditures have been constantly increasing in the last few years, the number of new drugs introduced into market has been steadily declining. This is mainly due to preclinical and clinical safety issues, which still represent about 40% of drug discontinuation. To cope with this issue, a number of in silico techniques are currently being used for an early stage evaluation/prediction of potential safety issues, allowing to increase the drug-discovery success rate and reduce costs associated with the development of a new drug. Methods: In the present review, we will analyse the early steps of the drug-discovery pipeline, describing the sequence of steps from disease selection to lead optimization and focusing on the most common in silico tools used to assess attrition risks and build a mitigation plan. Results: A comprehensive list of widely used in silico tools, databases, and public initiatives that can be effectively implemented and used in the drug discovery pipeline has been provided. A few examples of how these tools can be problem-solving and how they may increase the success rate of a drug discovery and development program have been also provided. Finally, selected examples where the application of in silico tools had effectively contributed to the development of marketed drugs or clinical candidates will be given. Conclusion: The in silico toolbox finds great application in every step of early drug discovery: (i) target identification and validation; (ii) hit identification; (iii) hit-to-lead; and (iv) lead optimization. Each of these steps has been described in details, providing a useful overview on the role played by in silico tools in the decision-making process to speed-up the discovery of new drugs.

Background: Computer-Aided Drug Design has strongly accelerated the development of novel antineoplastic agents by helping in the hit identification, optimization, and evaluation. Results: Computational approaches such as cheminformatic search, virtual screening, pharmacophore modeling, molecular docking and dynamics have been developed and applied to explain the activity of bioactive molecules, design novel agents, increase the success rate of drug research, and decrease the total costs of drug discovery. Similarity, searches and virtual screening are used to identify molecules with an increased probability to interact with drug targets of interest, while the other computational approaches are applied for the design and evaluation of molecules with enhanced activity and improved safety profile. Conclusion: In this review are described the main in silico techniques used in rational drug design of antineoplastic agents and presented optimal combinations of computational methods for design of more efficient antineoplastic drugs.

Background: The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology. Objective: Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions. Methods: We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions. Results: We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs. Conclusion: PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein–protein complexes for experimental studies.

Background: Protein aggregation into β-sheet-enriched insoluble assemblies is being found to be associated with an increasing number of debilitating human pathologies, such as Alzheimer’s disease or type 2 diabetes, but also with premature aging. Furthermore, protein aggregation represents a major bottleneck in the production and marketing of proteinbased therapeutics. Thus, the development of methods to accurately forecast the aggregation propensity of a certain protein is of much value. Methods/Results: A myriad of in vitro and in vivo aggregation studies have shown that the aggregation propensity of a certain polypeptide sequence is highly dependent on its intrinsic properties and, in most cases, driven by specific short regions of high aggregation propensity. These observations have fostered the development of a first generation of algorithms aimed to predict protein aggregation propensities from the protein sequence. A second generation of programs able to map protein aggregation on protein structures is emerging. Herein, we review the most representative online accessible predictive tools, emphasizing their main distinctive features and the range of applications. Conclusion: In this review, we describe representative biocomputational approaches to evaluate the aggregation properties of protein sequences and structures, while illustrating how they can become very useful tools to target protein aggregation in biomedicine and biotechnology.

Enzymes are a class of macromolecules that function as highly efficient and specific biological catalysts requiring only mild reaction conditions. Enzymes are essential to maintaining life activities, including promoting metabolism and homeostasis, and participating in a variety of physiological functions. Accordingly, enzymatic levels and activity are closely related to the health of the organism, where enzymatic dysfunctions often lead to corresponding diseases in the host. Due to this, diagnosis of certain diseases is based on the levels and activity of certain enzymes. Therefore, rapid real-time and accurate detection of enzymes in situ are important for diagnosis, monitoring, clinical treatment and pathological studies of disease. Fluorescent probes have unique advantages in terms of detecting enzymes, including being simple to use in highly sensitive and selective real-time rapid in-situ noninvasive and highly spatial resolution visual imaging. However, fluorescent probes are most commonly used to detect oxidoreductases, transferases and hydrolases due to the processes and types of enzyme reactions. This paper summarizes the application of fluorescent probes to detect these three types of enzymes over the past five years. In addition, we introduce the mechanisms underlying detection of these enzymes by their corresponding probes.

In recent years, fluorescent probes have recently attracted attention from researchers. As a vital trace metal element, Cu2+ has an important role in the human body and environment. Therefore, the development and design of Cu2+ small-molecular fluorescent probes has been an active research area. This review focuses on the developments in the area of small-molecular fluorescent probes for Cu2+ in biological applications according to different sensing mechanisms including charge transfer (CT), electron transfer, energy transfer, excited-state intramolecular proton transfer (ESIPT).

In recent years, aldehyde-appended fluorescence probes have attracted increasing attention. Fluorescent biological imaging includes many modern applications for cell and tissue imaging in biomedical research. Meanwhile, the nucleophilic mechanism is a very simple and convenient procedure for the preparation of aldehyde-sensing probes. This tutorial review focuses on aldehyde-bearing chemosensors based on nucleophilic addition mechanism with biological applications.

Luminescence bioimaging is widely used for noninvasive monitoring of biological targets in real-time with high temporal and spatial resolution. For efficient bioimaging in vivo, it is essential to develop smart organic dye platforms. Fluorescein (FL), a traditional dye, has been widely used in the biological and clinical studies. However, visible excitation and emission limited their further application for in vivo bioimaging. Nearinfrared (NIR) dyes display advantages of bioimaging because of their minimum absorption and photo-damage to biological samples, as well as deep tissue penetration and low auto-luminescence from background in the living system. Thus, some great developments of near-infrared fluorescein-inspired dyes have emerged for bioapplication in vitro and in vivo. In this review, we highlight the advances in the development of the near-infrared chemodosimeters for detection and bioimaging based on the modification of fluoresceininspired dyes naphtho-fluorescein (NPF) and cyanine-fluorescein (Cy-FL).

Self-assembled fluorescent nanomaterials based on small-molecule organic dyes are gaining increasing popularity in imaging and sensing applications over the past decade. This is primarily due to their ability to combine spectral properties tunability and biocompatibility of small molecule organic fluorophores with brightness, chemical and colloidal stability of inorganic materials. Such a unique combination of features comes with rich versatility of dye-based nanomaterials: from aggregates of small molecules to sophisticated core-shell nanoarchitectures involving hyperbranched polymers. Along with the ongoing discovery of new materials and better ways of their synthesis, it is very important to continue systematic studies of fundamental factors that regulate the key properties of fluorescent nanomaterials: their size, polydispersity, colloidal stability, chemical stability, absorption and emission maxima, biocompatibility, and interactions with biological interfaces. In this review, we focus on the systematic description of various types of organic fluorescent nanomaterials, approaches to their synthesis, and ways to optimize and control their characteristics. The discussion is built on examples from reports on recent advances in the design and applications of such materials. Conclusions made from this analysis allow a perspective on future development of fluorescent nanomaterials design for biomedical and related applications.

Pseudopeptides are privileged synthetic molecules built from the designed combination of peptide-like and abiotic artificial moieties. Consequently, they are benefited from the advantages of both families of chemical structures: modular synthesis, chemical and functional diversity, tailored three-dimensional structure, usually high stability in biological media and low non-specific toxicity. Accordingly, in the last years, these compounds have been used for different biomedical applications, ranging from bio-sensing, ion transport, the molecular recognition of biologically relevant species, drug delivery or gene transfection. This review highlights a selection of the most remarkable and recent advances in this field.